加入恶性疟原虫数据后,间日疟原虫抗原候选者预测结果有所改善。

IF 3.5 2区 生物学 Q1 MATHEMATICAL & COMPUTATIONAL BIOLOGY
Renee Ti Chou, Amed Ouattara, Shannon Takala-Harrison, Michael P Cummings
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引用次数: 0

摘要

在过去二十年里,由于大力控制和消灭疟疾,疟疾发病率大幅下降。然而,恶性疟原虫疟疾病例的减少导致了一些地理区域的物种转移,间日疟原虫在非洲以外的许多地区占据了主导地位。尽管间日疟原虫的地理分布广泛,但其疫苗的开发却远远落后于恶性疟原虫,部分原因是无法在体外培养间日疟原虫,从而阻碍了抗原鉴定的传统方法。在之前的一项研究中,我们使用了一种正向无标记随机森林(PURF)机器学习方法,根据已知抗原的特征识别恶性疟原虫抗原,供疫苗开发工作参考。在这里,我们整合了恶性疟原虫(研究较深入的物种)的系统数据,以改进 PURF 模型,从而预测潜在的间日疟原虫疫苗候选抗原。我们进一步表明,纳入其他物种的已知抗原对模型的性能至关重要,但只纳入其他物种的未标记蛋白质会导致模型误向物种分类预测,而不是抗原鉴定。除疟疾外,纳入近亲物种的抗原可能有助于为已知抗原很少或没有的新兴病原体开发疫苗。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Plasmodium vivax antigen candidate prediction improves with the addition of Plasmodium falciparum data.

Intensive malaria control and elimination efforts have led to substantial reductions in malaria incidence over the past two decades. However, the reduction in Plasmodium falciparum malaria cases has led to a species shift in some geographic areas, with P. vivax predominating in many areas outside of Africa. Despite its wide geographic distribution, P. vivax vaccine development has lagged far behind that for P. falciparum, in part due to the inability to cultivate P. vivax in vitro, hindering traditional approaches for antigen identification. In a prior study, we have used a positive-unlabeled random forest (PURF) machine learning approach to identify P. falciparum antigens based on features of known antigens for consideration in vaccine development efforts. Here we integrate systems data from P. falciparum (the better-studied species) to improve PURF models to predict potential P. vivax vaccine antigen candidates. We further show that inclusion of known antigens from the other species is critical for model performance, but the inclusion of only the unlabeled proteins from the other species can result in misdirection of the model toward predictors of species classification, rather than antigen identification. Beyond malaria, incorporating antigens from a closely related species may aid in vaccine development for emerging pathogens having few or no known antigens.

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来源期刊
NPJ Systems Biology and Applications
NPJ Systems Biology and Applications Mathematics-Applied Mathematics
CiteScore
5.80
自引率
0.00%
发文量
46
审稿时长
8 weeks
期刊介绍: npj Systems Biology and Applications is an online Open Access journal dedicated to publishing the premier research that takes a systems-oriented approach. The journal aims to provide a forum for the presentation of articles that help define this nascent field, as well as those that apply the advances to wider fields. We encourage studies that integrate, or aid the integration of, data, analyses and insight from molecules to organisms and broader systems. Important areas of interest include not only fundamental biological systems and drug discovery, but also applications to health, medical practice and implementation, big data, biotechnology, food science, human behaviour, broader biological systems and industrial applications of systems biology. We encourage all approaches, including network biology, application of control theory to biological systems, computational modelling and analysis, comprehensive and/or high-content measurements, theoretical, analytical and computational studies of system-level properties of biological systems and computational/software/data platforms enabling such studies.
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